Publications by authors named "Gabriel E Humpire-Mamani"
Article Synopsis
- Developed a fully automated deep learning algorithm for spleen segmentation in thorax-abdomen CT scans, using a dataset of 1100 scans from patients treated for cancer between 2014 and 2017.
- The algorithm showed comparable accuracy to independent radiologists in segmenting the spleen, with Dice scores of approximately 0.96.
- The use of the algorithm improved radiologists' agreement with a reference standard for detecting splenic volume changes from 81% to 92%.
Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient method for simultaneous localization of multiple structures in 3D thorax-abdomen CT scans. Our approach predicts the location of multiple structures using a single multi-label convolutional neural network for each orthogonal view.
View Article and Find Full Text PDF